Step 3 - Create a network graph/heatmap/bargraph from square matrix

#network library
require(visNetwork, quietly = TRUE)

package 㤼㸱visNetwork㤼㸲 was built under R version 3.4.4

library(reshape)

package 㤼㸱reshape㤼㸲 was built under R version 3.4.4
Attaching package: 㤼㸱reshape㤼㸲

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    rename

#we created a mirrored matrix, so we need to clean part of it (lower triangle), otherwise we´ll have duplicated values
new_matrix <- (Product_Matrix)
new_matrix[lower.tri(new_matrix)] <- NA
#View(new_matrix)
#create nodes and edges
nodes <- data.frame(id=colnames(Product_Matrix)[1:ncol(Product_Matrix)])
edges <- (melt(as.matrix(new_matrix)))
colnames(edges) <- c("from","to","value")
#View(nodes)
#View(edges)
#format edges$value to number
edges$value <- as.numeric(edges$value)
#delete NA values
edges <- edges[((!is.na(edges$value)) & edges$value>0),]
nodes$label <- nodes$id
edges$label <- edges$value
net_graph <- visNetwork(nodes, edges,height = "700px", width = "100%") %>%
  visIgraphLayout() %>%
  visNodes(size = 30) %>%
  visOptions(highlightNearest = TRUE,
             nodesIdSelection = TRUE) %>%
  visInteraction(keyboard = TRUE,
                 dragNodes = T, 
                 dragView = T, 
                 zoomView = T)

#view network graph
net_graph

#save network graph in html file
htmlwidgets::saveWidget(net_graph, "net_graph.html")

#matrix bargraph
barplot(Product_Matrix,beside=TRUE, legend=TRUE) 

library(plotly)

Carregando pacotes exigidos: ggplot2
package 㤼㸱ggplot2㤼㸲 was built under R version 3.4.4Want to understand how all the pieces fit together? See the R for Data Science book:
http://r4ds.had.co.nz/

Attaching package: 㤼㸱plotly㤼㸲

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    layout

#create a new data frame, to rank the frequency from edges dataframe
df_rank <- edges
#create a new column 'Description' joining columns 'From' and 'To'
df_rank$Description <- ifelse(df_rank$from != df_rank$to, paste(df_rank$from, df_rank$to, sep = " + "), as.character(df_rank$from))
#order dataframe by value
df_rank <- df_rank[order(-df_rank$value),]
#top 10 values
if (nrow(df_rank)>=10){
  df_rank_10 <- df_rank[1:10,]
}else{
  df_rank_10 <- df_rank
}
plot_ly(data = df_rank_10,
     x = ~Description,
     y = ~value,
     type = "bar"
) %>%
layout(
     title = "Frequency rank of product combinations",
     xaxis = list(title = "",
                  categoryorder = "array",
                  categoryarray = ~value,
                  tickangle=-45),
     yaxis = list(title = "Sum of occurrences"),
     margin = list(b = 200)
)

#heatmap
library(plotly)
 
data=as.matrix(Product_Matrix)
hm1 <- plot_ly(x=colnames(data), y=rownames(data), z = data, type = "heatmap")
 
# with normalization
data=apply(data, 2, function(x){x/mean(x)})
plot_ly(x=colnames(data), y=rownames(data), z = data, type = "heatmap")

#save network graph in html file
htmlwidgets::saveWidget(hm1, "heatmap1.html")

#export result
write.csv(df_rank,"Output/rank-combinations.csv")

library(ggplot2)
df_hm <- df_rank[,(1:3)]
ggplot(data = df_hm, aes(x=from, y=to, fill=value)) + 
  geom_tile()

#correlation of occurrences
cormat <- round(cor(Product_Matrix),2)
head(cormat)

        Pants Shoes Socks t-shirt
Pants    1.00 -0.17  0.82   -0.58
Shoes   -0.17  1.00 -0.43    0.90
Socks    0.82 -0.43  1.00   -0.71
t-shirt -0.58  0.90 -0.71    1.00

library(reshape2)

Attaching package: 㤼㸱reshape2㤼㸲

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melted_cormat <- melt(cormat)
head(melted_cormat)

library(ggplot2)
ggplot(data = melted_cormat, aes(x=X1, y=X2, fill=value)) + 
  geom_tile()